Method and system for effectively performing event detection in a large number of concurrent image sequences

ABSTRACT

Method and system for performing event detection and object tracking in image streams by installing in field, a set of image acquisition devices, where each device includes a local programmable processor for converting the acquired image stream that consist of one or more images, to a digital format, and a local encoder for generating features from the image stream. These features are parameters that are related to attributes of objects in the image stream. The encoder also transmits a feature stream, whenever the motion features exceed a corresponding threshold. Each image acquisition device is connected to a data network through a corresponding data communication channel. An image processing server that determines the threshold and processes the feature stream is also connected to the data network. Whenever the server receives features from a local encoder through its corresponding data communication channel and the data network, the server provides indications regarding events in the image streams by processing the feature stream and transmitting these indications to an operator.

FIELD OF THE INVENTION

The present invention relates to the field of video processing. Moreparticularly, the invention relates to a method and system for obtainingmeaningful knowledge, in real time, from a plurality of concurrentcompressed image sequences, by effective pro ng of a large number ofconcurrent incoming image sequences and/or features derived from theacquired images.

BACKGROUND OF THE INVENTION

Many efforts have been spent to improve the ability to extract meaningdata out of images captured by video and still cameras. Such abilitiesare being used in several applications, such as consumer, industrial,medical, and business applications. Many cameras are deployed in thestreets, airports, schools, banks, offices, residencies—as standardsecurity measures. These cameras are used either for allowing anoperator to remotely view security events in real time, or for recordingand analyzing a security event at some later time.

The introduction of new technologies is shifting the video surveillanceindustry into new directions that significantly enhance thefunctionality of such systems. Several processing algorithms are usedboth for real-time and offline applications. These algorithms areimplemented on a range of platforms from pure software to pure hardware,depending on the application. However, these platforms are usuallydesigned to simultaneously process a relatively small number of incomingimage sequences, due to the substantial computational resources requiredfor image processing. In addition, most of the common image processingsystems are designed to process only uncompressed image data, such asthe system disclosed in U.S. Pat. No. 6,188,381. Modern networked videoenvironments require efficient processing capability of a large numberof compressed video steams, collect from a plurality of image sources.

Increasing operational demands, as well as cost constrains created theneed for automation of event detection. Such event detection solutionsprovide a higher detection level, save manpower, replace other types ofsensors and lower false alarm rates.

Although conventional solutions am available for automatic intruderdetection, license plate identification, facial recognition, trafficviolations detection and other image based applications, they usuallysupport few simultaneous video sources, using expensive hardwareplatforms that require field installation, which implies highinstallation, maintenance and upgrade costs.

Conventional surveillance systems employ digital video networkingtechnology and automatic event detection. Digital video networking isimplemented by the development of Digital Video Compression technologyand the availability of IP based networks. Compression standards, suchas MPEG-4 and similar formats allow transmitting high quality imageswith a relatively narrow bandwidth.

A major limiting factor when using digital video networking is bandwidthrequirements. Because it is too expensive to transmit all the camerasall the time, networks are designed to concurrently transmit data, onlyfrom few cameras. The transmission of data only from cameras that arecapturing important events at any given moment is crucial forestablishing an efficient and cost effective digital video network.

Automatic video-based event detection technology becomes effective forthis purpose. This technology consists of a series of algorithms thatare able to analyze the camera image in real time and providenotification of a special event, if it occurs. Currently availableevent-detection solutions use conventional image processing methods,which require heavy processing resources. Furthermore, they allocate afixed processing power (usually one processor) per each camera input.Therefore, such systems either provide poor performance due to resourceslimitation or are extremely expensive.

As a result, the needs of large-scale digital surveillanceinstallations—namely, reliable detection, effective bandwidth usage,flexible event definition, large-scale design and cost, cannot be met byany of the current automatic event detection solutions.

Video Motion Detection (VMD) methods are disclosed, for example, in U.S.Pat. No. 6,349,114, WO 02/37429, in U.S. Patent Application Publication2002,041,626, in U.S. Patent Application Publication No. 2002,054,210,in WO 01/63937, in EP1107609, in EP1173020, in U.S. Pat. No. 6,384,862,in U.S. Pat. No. 6,188,381, in U.S. Pat. No. 6,130,707, and in U.S. Pat.No. 6,069,655. However, all the methods described above have not yetprovided satisfactory solutions to the problem of effectively obtainingmeanings knowledge, in real time, from a plurality of concurrent imagesequences.

It is an object of the present invention to provide a method and systemfor obtaining meaningful knowledge, from a plurality of concurrent imagesequences, in real time.

It is another object of the present invention to provide a method andsystem for obtaining meaningful knowledge, from a plurality ofconcurrent image sequences, which are cost effective.

It is a further object of the present invention to provide a method andsystem for obtaining meaningful knowledge, from a plurality ofconcurrent image sequences, with reduced amount of bandwidth resources.

It is still another object of the present invention to provide a methodand system for obtaining meaningful knowledge, from a plurality ofconcurrent image sequences, which is reliable, and having highsensitivity in noisy environments.

It is yet another object of the present invention to provide a methodand system for obtaining meaningful knowledge, from a plurality ofconcurrent image sequences, with reduced installation and maintenancecosts.

Other objects and advantages of the invention will become apparent asthe description proceeds.

SUMMARY OF THE INVENTION

While these specifications discuss primarily video cameras, a personskilled in the art will recognize that the invention extends to anyappropriate image source, such as still cameras, computer generatedimages, prerecorded video data, and the like, and that image sourcesshould be equivalently considered. Similarly, the terms video and videostream, should be construed broadly to include video sequences, stillpictures, computer generated graphics) or any other sequence of imagesprovided or converted to an electronic format that may be processed by acomputer.

The present invention is directed to a method for performing eventdetection and object tracking in image streams. A set of imageacquisition devices is installed in field, such that each devicecomprises a local programmable processor for converting the acquiredimage stream, that consists of one or more images, to a digital format,and a local encoder, for generating features from the mage stream. Thefeatures are parameters that are related to attributes of objects in theimage stream. Each device transmits a feature stream, whenever thenumber and type of features exceed a corresponding threshold. Each imageacquisition device is connected to a data network through acorresponding data communication channel. An image processing serverconnected to the data network determines the threshold and processes thefeature stream. Whenever the server receives features from a localencoder through its corresponding data communication channel and thedata network, the server obtains indications regarding events in theimage streams by processing the feature stream and transmitting theindications to an operator.

The local encoder may be a composite encoder, which is a local encoderthat further comprises circuitry for compressing the image stream. Thecomposite encoder may operate in a first mode, during which it generatesand transmits the features to the server, and in a second mode, duringwhich it transmits to the server, in addition to the features, at leasta portion of the image stream in a desired compression level, accordingto commands sent from the server. Preferably, each composite encoder iscontrolled by a command sent from the server, to operate in its firstmode. As long as the server receives features from a composite encoder,that composite encoder is controlled by a command sent from the server,to operate in its second mode. The server obtains indications regardingevents in the image streams by processing the feature stream, andtransmitting the indications and/or their corresponding image streams toan operator.

Whenever desired one or more compressed image streams containing eventsare decoded by the operator station, and the decoded image streams aretransmitted to the display of an operator, for viewing. Compressed imagestreams obtained while their local encoder operates in its second modemay be recorded.

Preferably, additional image processing resources, in the server, aredynamically allocated to data communication duels that receive imagestreams. Feature streams obtained while operating in the first mode maycomprise only a portion of the image.

A graphical polygon that encompasses an object of interest being withinthe fame of an image or an AOI (Area Of Interest) in the image may begenerated by the server and displayed to an operator for viewing. Inaddition, the server may generate and display a graphical traceindicating the history of movement of an object of interest, beingwithin the frame of an image or an AOI in the image.

The image stream may be selected from the group of images that comprisesvideo streams, still images, computer generated images, and pre-recordeddigital, analog video data, or video streams, compressed using MPEGformat. The encoder may use different resolution and frame rate duringoperation in each mode.

Preferably, the features may include motion features, color, portions ofthe image, edge data and frequency related information.

The server may perform, using a feature stream, received from the localencoder of at least one image acquisition device, one or more of thefollowing operations and/or any combination thereof:

-   -   License Plate Recognition (LPR);    -   Facial Recognition (FR);    -   detection of traffic rules violations;    -   behavior recognition;    -   fire detection;    -   traffic flow detection;    -   smoke detection.

The present invention is also directed to a system for performing eventdetection and object tracking in image streams, that comprises:

-   -   a) a set of image acquisition devices, ins ed in field, each of        which includes:        -   a.1) a local programmable processor for converting the            acquired image stream, to a digital format        -   a.2) a local encoder, for generating, from the image stream,            features, being parameters related to attributes of objects            in the image stream, and for transmitting a feature stream,            whenever the motion features exceed a corresponding            threshold;    -   b) a data network, to which each image acquisition device is        connected through a corresponding data communication channel;    -   c); and    -   d) an image processing server connected to the data network, the        server being capable of determining the threshold, of obtaining        indications regarding events in the image streams by processing        the feature stream, and of transmitting the indications to an        operator.

The system may further comprise an operator display, for receiving anddisplaying one or more image streams that contain events, as well as anetwork video recorder for recording one or more image streams, obtainedwhile their local encoder operates in its first mode.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other characteristics and advantages of the invention willbe better understood through the following illustrative andnon-limitative detailed description of preferred embodiments thereof,with reference to the appended drawings, wherein:

FIG. 1 schematically illustrates the structure of a surveillance systemthat comprises a plurality of cameras connected to a data network,according to a preferred embodiment of the invention;

FIG. 2 illustrates the use of AOI's (Area of Interest) for designatingareas where event detection will be performed and for reducing the usageof system resources, according to a preferred embodiment of theinvention; and

FIGS. 3A to 3C illustrates the generation of an object of interest andits motion trace, according to a preferred embodiment of the invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

A significant saving in system resources can be achieved by applyingnovel data reduction techniques, proposed by the present invention. In asituation where thousands of cameras are connected to a single server,only a small number of the cameras actually acquire important eventsthat should be analyzed. A large-scale system can function properly onlyif it has the capability of identifying the inputs that may containuseful information and perform further processing only on such inputs.Such a filtering mechanism requires minimal processing and bandwidthresources, so that it is possible to apply it concurrently on a largenumber of image streams. The present invention proposes such a filteringmechanism, called Massively Concurrent Image Processing (MCIP)technology that is based on the analysis of incoming image sequencesand/or feature streams, derived from the acquired images, so as tofulfill the need for automatic image detection capabilities in alarge-scale digital video network environment.

MCIP technology combines diverse technologies such as large scale datareduction, effective server design and optimized image processingalgorithms, thereby offering a platform that is mainly directed to thesecurity market and is not rivaled by conventional solutions,particularly with vast numbers of potential users. MCIP is a networkedsolution for event detection in distributed installations, which isdesigned for large scale digital video surveillance networks thatconcurrently support thousands of camera inputs, distributed in anarbitrarily large geographical area and with real time performance. MCIPemploys a unique feature transmission method that consumes narrowbandwidth, while maintaining high sensitivity and probability ofdetection. MCIP is a server-based solution that is compatible withmodern monitoring and digital video recording systems and carries outcomplex detection algorithms, reduces field maintenance and providesimproved scalability, high availability, low cost per channel and backuputilities. The same system provides concurrently multiple applicationssuch as VMD, LPR and FR. In addition, different detection applicationsmay be associated with the same camera.

MCIP is composed of a server platform with various applications, cameraencoders (either internal or eternal to the camera), a Network VideoRecorder (NVR) and an operator station. The server contains a computerthat includes proprietary hardware and software components. MCIP isbased on the distribution of image processing algorithms betweenlow-level feature extraction, which is performed by the encoders whichare located in field (i.e., in the vicinity of a camera), and high-levelprocessing applications, which are performed by a remote central serverthat collects and analyzes these features.

The MCIP system described hereafter solves not only the bandwidthproblem but also reduces the load from the server and uses a unique typeof data stream (not a digital video stream), and performs an effectiveprocess for detecting events at real time, in a large scale videosurveillance environment.

A major element in MCIP is data reduction, which is achieved by thedistribution of the image processing algorithms. Since all the videosources, which require event detection, transmit concurrently, therequired network bandwidth is reduced by generating a reduced bandwidthfeature stream in the vicinity of each camera. In order to detect andtrack moving objects in digitally transmitted video sources by analyzingthe transmitted reduced bandwidth feature, there is no need to transmitfull video streams, but only partial data, which contains informationregarding moving objects.

By doing so, a significantly smaller data bandwidth is used, whichreduces the demands for both the network bandwidth and the eventdetection processing power. Furthermore, if only the shape, size,direction of movement and velocity should be detected, there is no needto transmit data regarding their intensity or color, and thus, a furtherbandwidth reduction is achieved. Another bandwidth optimization may beachieved if the encoder in the transmitting side filters out all motionswhich are under a motion threshold, determined by the remote centralserver. Such threshold may be the AC level of a moving object, motiondistance or any combination the and may be determined and changeddynamically, according to the attributes of the acquired image, such asresolution, AOI, compression level, etc. Moving objects which are underthe threshold are considered either as noise, or non-interestingmotions.

One method for extracting features at the encoder side is by slightlymodifying and degrading existing temporal-based video compressors whichwere originally designed to transmit digital video. The features mayalso be generated by a specific feature extraction algorithm (such asany motion vector generating algorithm) that is not related to the videocompression algorithm. When working in this reduced bandwidth mode, theoutput streams of these encoders are definitely not a video stream, andtherefore cannot not be used by any receiving party to produce videoimages.

FIG. 1 schematically illustrates the structure of a surveillance systemthat comprises a plurality of cameras connected to a data network,according to a preferred embodiment of the invention. The system 100comprises n image sources (in this example, n cameras, CAM1, . . .,CAMn), each of which connected to a digital encoder ENCj, or convertingthe images acquired by CAMj to a compressed digital format. Each digitalencoder ENCj is connected to a digital data network 101 at point pj andbeing capable of transmitting data, which may be a reduced bandwidthfeature stream or a full compressed video stream, through itscorresponding channel Cj. The data network 101 collects the datatransmitted from all channels and forwards them to the MCIP server 102,through data-bus 103. MCIP server 102 processes the data received fromeach channel and controls one or more cameras which transit anycombination of the reduced bandwidth feature stream and the fullcompressed video stream, which can be analyzed by MCIP server 102 inreal time, or recorded by NVR 104 and analyzed by MCIP server 102 later.An operator station 105 is also connected to MCIP server 102, for realtime monitoring of selected fill compressed video streams. Operatorstation 105 can manually control the operation of MCIP server 102,whenever desired.

The MCIP (Massively Concurrent Image Processing) server is connected tothe image sources (depicted as cameras in the drawing, but may also beany image source, such taped video, still cameras, video cameras,computer generated images or graphics, and the like.) through data-bus103 and network 101, and receives features or images in a compressedformat. In the broadest sense this is any type of network, wired orwireless. The images can be compressed using any type of compression.Practically, WP based networks are used, as well as compression schemesthat use DCT, VideoLAN Client (VLC, which is a highly portablemultimedia player for various audio and video formats as well as DigitalVersatile Discs (DVDs), Video Compact Discs (VCDs), and variousstreaming protocols, disclosed in WO 01/63937) and motion estimationtechniques such as MPEG.

The system 100 uses an optional load-balancing module that allows it toeasily scale the number of inputs that can be processed and also createsthe ability to remove a single point of failure, by creating backup MCIPservers. The system 100 also has a configuration component that is usedfor defining the type of processing that should be performed for eachinput and the destination of the processing res. The destination can beanother computer, an email address, a monitoring application, or anyother device that is able to receive textual and/or visual messages.

The system can optionally be connected to an external database to assistimage processing. For example, a database of suspect, stolen cars, oflicense plate numbers can be used for identifying vehicles.

FIG. 2 illustrates the use of AOI's (Area of Interest) for reducing theusage of system resources, according to a preferred embodiment of theinvention. An AOI is a polygon (in this Fig., an hexagon) that enclosesthe area where detection will occur. The rectangles indicate theestimated object size at various distances from the camera. In thisexample, the scene of interest comprises detection movement of a personin a field (shown in the first rectangle). It may be used in thefiltering unit to decide if further processing is required. In thiscase, the filtering unit examines the feature data. The feature streamis analyzed to determine if enough significant features lie within theAOI. If the number of features that are located inside the AOI andcomprise changes, exceeds the threshold, then this frame is designatedas possibly containing an event and is transferred for furtherprocessing. Otherwise, the frame is dropped and no further processing isperformed.

The MCIP server receives the reduced bandwidth feature stream (such afeature stream is not a video stream at all, and hence, no viewableimage can be reconstructed thereof) from all the video sources whichrequire event detection. When an event is detected within a reducedbandwidth stream that is transmitted from a specific video source, thecentral server may instruct this video source to change its operationmode to a video stream mode, in which that video source may operate as aregular video encoder and transmits a standard video stream, which maybe decoded by the server or by any receiving party for ovation,recording, further processing or any other purpose. Optionally the videoencoder also continues transmitting the feature stream at the same time.

Working according to this scheme, most of the video sources remain inthe reduced bandwidth mode, while transmitting a narrow bandwidth datastream, yet sufficient to detect events with high resolution and framerate at the MCIP server. Only a very small portion of the sources (inwhich event is detected) are controlled to work concurrently in thevideo stream mode. This results in a total network bandwidth, which issignificantly lower than the network bandwidth required for concurrentlytransmitting from all the video sources.

For example, if a conventional video surveillance installation that uses1000 cameras, a bandwidth of about 500 Kbp/s is needed by each camera,in order to transmit at an adequate quality. In the reduced bandwidthmode, only about 5 Kbp/s is required by each camera for the transmissionof information regarding moving objects at the same resolution and framerate. Therefore, all the cameras working in this mode are using a totalbandwidth of 5 Kbp/s times 1000=5 Mbp/s. Assuming that at steady statesuspected objects appear in 1% of the cameras (10 cameras) and they areworking in video strum mode, extra bandwidth of 10 times 500 Kbp/s=5Mbp/s is required. Thus, the total required network bandwidth using thesolution proposed by the present invention is 10 Mbp/s. A total requirednetwork bandwidth of 500 Mbp/s would be consumed by conventionalsystems, if all the 1000 cameras would concurrently transmit videostreams.

The proposed solution may be applicable not only for high-level movingobjects detection and tracking in live cameras but also in recordedvideo. Huge amounts of video footage are recorded by many surveillancesystems. In order to detect interesting events in this recorded video,massive processing capabilities are needed. By converting recordedvideo, either digital or analog, to a reduced bandwidth stream accordingto the techniques described above, event detection becomes much easier,with lower processing requirements and faster operation.

The system proposed in the present invention comprises the followingcomponents:

-   1. One or more MCIP servers-   2. One or more dual mode video encoders, which may be operated at    reduced bandwidth mode or at video stream mode, according to remote    instructions.-   3. Digital network, LAN or WAN, IP or other, which establishes    communication between the system components.-   4. One or more operator stations, by which operators may define    events criteria and other system parameters and manage events in    real time.-   5. An optional Network Video Recorder (NVR), which is able to record    and play, on demand, any selected video source which is available on    the network.    Implementation for Security Applications:

Following is a partial list of types of image processing applicationswhich can be implemented very effectively using the method proposed bythe present invention:

Video Motion Detection—for both indoor and outdoor applications. Suchapplication is commonly used to detect intruders to protected zones. Itis desired to ignore nuisances such as moving trees, dust and animals.In this embodiment of the present invention manipulates input images atthe stream level in order to filter out certain images and imagechanges. Examples of such filtering are motion below a predeterminedthreshold, size or speed related filtering all preferably applied withinthe AOIs, thus reducing significantly the amount of required systemresources for further processing. Since the system is server-based andthere is no need for installation of equipment in the field (except thecamera), this solution is very attractive for low budget applicationsuch as in the residential market.

Exceptional static objects detection—this application is used to detectstatic objects where such objects may require an alarm, By way ofexample, such objects may comprise an unattended bag at the airport, astopped car on a highway, a person stopped at a protected location andthe like. In this embodiment the present invention manipulates the inputimages at the stream level and examines the motion vectors at the AOIs.Objects that stopped moving are further processed

License Plate Recognition—this application is used for vehicles accesscontrol, stolen or suspected car detection and parking automation. Inthis embodiment, it is possible to detect wanted cars using hundreds ormore cameras installed in the field, thus providing a practicaldetection solution.

Facial Recognition—this application is desired for biometricverification or detection device, for tasks such as locating criminalsor terrorists and for personal access control purposes. Using thisembodiment offers facial recognition capability to many cameras in thefield. This is a very useful tool for large installations such asairports and public surveillance.

Smoke and flames detection—this application is used for fire detection.Using this embodiment of the invention, all the sites equipped withcameras may receive this service in addition to other applicationwithout any installation of smoke or flame detectors.

Traffic violations—this application detect a variety of trafficviolation such as red light crossing, separation line crossing, parkingor stopping at forbidden zone and the like. Using this embodiment, thisfunctionality may be applied for many cameras located along roads andintersections, thus significantly optimizing police work.

Traffic flow analysis—this application is useful for traffic centers byautomatically detecting any irregular traffic events such as trafficobstacles, accidents, too slow or too fast or too crowded traffic andthe like. Using this embodiment, traffic centers may use many cameraslocated as desired at the covered area in order to provide asignificantly better control level.

Suspicious vehicle or person tracking—this application is used to trackobjects of interest. This is needed to link a burglar to an escape car,locate a running suspect and more. Using this embodiment, thisfunctionality may be associated with any selected camera or cameras inthe field.

It should be noted that each of those applications or their combinationmay each be considered as a separate embodiment of the invention, allwhile using the basic structure contemplated herein, while specificembodiments may utilize specialized components. Selection of suchcomponent and the combination of features and applications providedherein is a matter of technical choice that will be clear to thoseskilled in the art.

FIGS. 3A to 3C illustrate the generation of an object of interest andits motion trace, according to a preferred embodiment of the invention.FIG. 3A is an image of a selected AOI (m this example, an elongatedzone, in which the presence of any person is forbidden), on which theMCIP server 102 generates an object, which is determined according toredefined size and motion parameters, received from the correspondingencoder. The object encompasses the body of a person, penetrating intothe forbidden zone and walking from right to left. The motion parametersare continuously updated, such that the center of the object is tracked.The MCIP server 102 generates a trace (solid line) that provides agraphical indication regarding his motion within the forbidden zone.FIG. 3B is an image of the same selected AOI, on which the MCIP server102 generates the object and the trace (solid line) that provides agraphical indication regarding his motion within the forbidden zone fromleft to right and more closely to the camera. FIG. 3C is an image of thesame selected AOI, on which the MCIP server 102 generates the object andthe trace (solid line) that provides a graphical indication regardinghis motion within the forbidden zone again from right to left and moreclosely to the camera. The filtration performed by the correspondingencoder prevents the generation of background movements, such astree-tops and lower vegetation, which are considered as backgroundnoise.

The above examples and description have of course been provided only firthe purpose of illustration, and are not intended to limit the inventionin any way. As will be appreciated by the skilled person, the inventioncan be carried out in a great variety of ways, employing more than onetechnique from those described above, all without exceeding the scope ofthe invention.

1. Method for performing event detection and object tracking in imagestreams, comprising: a) installing in field, a set of image acquisitiondevices, each of which comprising a local programmable processor forconverting the acquired image stream, consisting of one or more images,to a digital format, and a local encoder, for generating, from saidimage stream, features, being parameters related to attributes ofobjects in said image stream, and for transmitting a feature stream,whenever said motion features exceed a corresponding threshold; b)connecting each image acquisition device to a data network through acorresponding data communication channel; c) connecting an imageprocessing server to said data network, said server being capable ofdetermining said threshold, and of processing said feature stream; andd) whenever said server receives features from a local encoder throughits corresponding data communication channel and said data network,obtaining indications regarding events in said image streams byprocessing, by said server, said feature stream, and transmitting saidindications to an operator.
 2. Method according to claim 1, wherein thelocal encoder is a composite encoder, being the local encoder thatfurther comprises circuitry for compressing the image stream, saidcomposite encoder being capable of operating in a first mode, duringwhich it generates and transmits the features to the server, and in asecond mode, during which it transmits to said server, in addition tosaid features, at least a portion of said image stream in a desiredcompression level, according to commands sent from said server. 3.Method according to claim 2, further comprising, controlling eachcomposite encoder, by a command sent from said server, to operate in itsfirst mode; as long as the server receives features from a compositeencoder: a) controlling that composite encoder, by a command sent fromsaid server, to operate in its second mode; and b) obtaining indicationsregarding events in said image streams by processing, by said server,said feature stream, and transmitting said indications and/or theircorresponding image streams to an operator.
 4. Method according to claim1, further comprising decoding one or more compressed image streamscontaining events by said server, and transmitting the decoded imagestreams to the display of an operator, for viewing.
 5. Method accordingto claim 2, further comprising recording one or more compressed imagestreams obtained while their local encoder operates in its second mode.6. Method according to claim 2, further comprising dynamicallyallocating additional image processing resources, in the server, to datacommunication channels that receive image streams.
 7. Method accordingto claim 1, wherein one or more feature streams obtained while operatingin the first mode, comprises only a portion of the image.
 8. Methodaccording to claim 6, further comprising generating and displaying agraphical polygon that encompasses an object of interest, being withinthe frame of an image or an AOI in said image.
 9. Method according toclaim 8, further comprising generating and displaying a graphical traceindicating the history of movement of an object of interest, beingwithin the frame of an image or an AOI in said image.
 10. Methodaccording to claim 1, wherein the image stream is selected from thegroup of images that comprises video streams, still images, computergenerated images, and pre-recorded digital or analog video data. 11.Method according to claim 1, wherein the image streams are videostreams, compressed using MPEG format.
 12. Method according to claim 2,wherein during each mode, the encoder uses different resolution andframe rate.
 13. Method according to claim 1, wherein the features areselected from the following group: motion features; color, portion ofthe image; edge data; and frequency related information.
 14. Methodaccording to claim 1, further comprising performing, by the server, oneor more of the following operations and/or any combination thereof:License Plate Recognition (LPR); Facial Recognition (FR); detection oftraffic rules violations; behavior recognition; fire detection; trafficflow detection; smoke detection, using a feature stream, received fromthe local encoder of at least one image acquisition device, through itsdata communication channel
 15. System for performing event detection andobject tracking in image streams, comprising: a) a set of imageacquisition devices, installed in field, each of which includes: a. 1) alocal programmable processor for converting the acquired image stream,to a digital format a.2) a local encoder, for generating, from saidimage stream, features, being parameters related to attributes ofobjects in said image stream, and for transmitting a feature stream,whenever said motion features exceed a corresponding threshold; b) adata network, to which each image acquisition device is connectedthrough a corresponding data communication channel; c); and d) an imageprocessing server connected to said data network, said server beingcapable of determining said threshold, of obtaining indicationsregarding events in said image streams by processing said featurestream, and of transmitting said indications to an operator.
 16. Systemaccording to claim 15, in which the local encoder is a compositeencoder, being the local encoder that further comprises circuitry forcompressing the image stream, said composite encoder being capable ofoperating in a first mode, during which it generates and transmits thefeatures to the server, and in a second mode, during which it transmitsto said server, in addition to said features, at least a portion of saidimage stream in a desired compression level, according to commands sentfrom said server.
 17. System according to claim 15, further comprisingan operator display, for receiving one or more image steams that aredecoded by the server and contain events.
 18. System according to claim16, fixer comprising a network video recorder for recording one or moreimage streams, obtained while their local encoder operates in its firstmode.
 19. System according to claim 15, in which the server is capableof dynamically allocating additional image processing resources to datacommunication channels that receive image streams.
 20. System accordingto claim 16, in which one or more image streams obtained while operatingin the first mode, comprises only a portion of the image thatcorresponds to a desired AOI
 21. System according to claim 15, in whichthe server further comprises processing means for generating anddisplaying a graphical polygon that encompasses an object of interest,being within the frame of an image or an AOI in said image.
 22. Systemaccording to claim 21, in which the server further comprises processingmeans for generating and displaying a graphical trace indicating thehistory of movement of an object of interest, being within the frame ofan image or an AOI in said image.
 23. System according to claim 15, inwhich the image stream is selected from the group of images thatcomprises video streams, still images, computer generated images, andpre-recorded digital or analog video data.
 24. System according to claim15, in which the image streams are video streams, compressed using MPEGformat.
 25. System according to claim 16, in which during each mode, theencoder uses different resolution and frame rate.
 26. System accordingto claim 15, in which the features are selected from the followinggroup: motion features; color; portion of the image; edge data; andfrequency related information.
 27. System according to claim 15, inwhich the server further comprises processing means for performing oneor more of the following operations and/or any combination thereof:License Plate Recognition (LPR); Facial Recognition (FR); detection oftraffic rules violations; behavior recognition; fire detection; trafficflow detection; smoke detection, using a feature stream, received fromthe local encoder of at least one image acquisition device, through itsdata communication channel.
 28. Method for performing event detectionand object tracking in image streams, substantially as described andillustrated.
 29. System for performing event detection and objecttracking in image streams, substantially as described and illustrated.